INSP Process Engineer

Role Purpose

• Following one engineer’s departure in May, Sufficient inspection engineering resources are essential for supporting both flexible production modes and ongoing project growth, need to request a new senior level INSP process engineer to backfill this HC gap.
• Meanwhile, seek a senior and digitally fluent engineering leader to drive the Finishing INSP (Inspection) Process function at our High Flexibility Plant. The role is accountable for the technical performance, quality outcomes, and continuous improvement of our INSP lines, while leading a small team of process and control engineers.

Key Responsibilities

• [INSP process ownership]:

• • Establishes INSP operation process and sustains daily performance, troubleshooting while performance upset or equipment shut down. Support INSP equipment and control system improvement and develop the most efficient production technology. Design, modify, and recommend equipment types to meet or exceed requirements in operation
  • Lead MEE / DMAIC root-cause investigations for systemic INSP issues; drive containment, fundamental understanding, and permanent corrective actions.
  • Define and maintain KPIVs / KPOVs, control limits, and SPC monitoring strategies.

• [Team leadership & Talent development]:

• • Lead a team of 3–5 process and control engineers; set goals and build individual development plans.
  • Build technical bench depth — identify SME candidates, coach successors, and mentor junior engineers.
  • Drive cross-functional collaboration with CBW Process, Pack, Quality, Maintenance, IT, and corporate engineering teams.
  • Foster a culture of structured problem solving, data-driven decisions, and continuous learning.

• [Digital & AI Transformation]
  • Champion digital transformation of the INSP process — deploy data analytics, anomaly detection (e.g., Robust-Z), computer vision, and machine-learning models to enhance defect classification, predictive control, and smart-factory integration.
  • Partner with the Digital (D^t) team and corporate IT to design and implement scalable data pipelines, dashboards, and ML/AI applications.
  • Promote AI-assisted root-cause analysis (e.g., LLM-based knowledge mining, image-AI for defect FA) across the engineering team.

Experiences/Education - Required

• Bachelor's degree or above in Mechanical, Materials, Electrical, Chemical Engineering, or a related discipline.
• 5+ years of process or equipment engineering experience in display, semiconductor, or precision-manufacturing industry.
• 3+ years of direct people-leadership experience — formal team lead, supervisor, or section manager — including PDR ownership, hiring, and coaching.

   

• Required-Industry experience 

  • Demonstrated track record of leading complex technical investigations (6-sigma, 8D, or equivalent) to a successful quantitative outcome.
  • Strong process control fundamentals: SPC, CPK, hypothesis testing, KPIV/KPOV methodology.
  • Working fluency in English (written and verbal) for cross-region collaboration.

   

• Required — Digital / AI Capability 

  • Hands-on competence in data analytics tools — Python (pandas / scikit-learn), SQL, JMP, Minitab, or equivalent.
  • Experience designing or deploying at least one digital, data, or AI project in a manufacturing context — e.g., predictive maintenance, computer-vision defect classification, anomaly detection, or Industry 4.0 / smart-factory pilots.
  • Familiarity with modern AI/ML concepts — supervised / unsupervised models, computer vision, LLMs, model lifecycle.
  • Ability to partner with data scientists, IT, or corporate digital teams to deliver end-to-end solutions.

Experiences/Education - Desired

• Master’s degree in engineering, Materials Science, Computer Science, or Data Science.
• Lean Six Sigma Green Belt or Black Belt certification.
• Experience with display-glass finishing processes — CBW, R-grinding, EIS, ADEP, PIS, MRS.
• Prior experience leading digital transformation programs at the plant or business-unit level.
• Exposure to MES, PLC, IoT platforms, or edge-computing architectures.
• Demonstrated publication, patent, or sister-plant migration of engineering best practices.